Sampling Distribution of a Sample Mean Homework 1. Suppose that the number of texts sent during a typical day by a randomly selected high school student follows a right-skewed distribution with a mean of 15 and a standard deviation of 35 . Assuming that students at your school are typical texters, how likely is it that a random sample of 50 students will have sent more than a total of 1000 texts in the last 24 hours? 2. The number of absences last year for students at a large high school has a mean of 5.6 days with a standard deviation of 4.1 days. Suppose we take an SRS of 80 of last year's students and calculate the mean number of absences for the members of the sample. (Refer to example of page 470 in your textbook) (a) Identify the mean of the sampling distribution of \( \bar{x} \). (b) Calculate and interpret the standard deviation of the sampling distribution of \( \bar{x} \). Verify that the \( 10 \% \) condition is met.
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To tackle the first problem, we will utilize the Central Limit Theorem, which tells us that as the sample size increases, the sampling distribution of the sample mean will approach a normal distribution, even if the original population is not normal. With a sample size of 50 students and a mean of 15 texts per student, that gives us a total expected number of texts of \( 15 \times 50 = 750 \). The standard deviation of the sample mean will be \( \frac{35}{\sqrt{50}} \approx 4.95 \). To assess how likely it is to exceed 1000 texts, calculate the z-score: \( z = \frac{1000 - 750}{4.95} \approx 50.51 \). The probability of this z-score occurring is exceedingly low, indicating it's highly unlikely that 50 students sent more than 1000 texts in total. Now, for the second problem regarding absences, the mean of the sampling distribution of \( \bar{x} \) is simply the same as the population mean, which is 5.6 days. For the standard deviation of the sampling distribution of \( \bar{x} \) (also known as the standard error), you can use the formula \( \frac{\sigma}{\sqrt{n}} \), where \( \sigma = 4.1 \) days and \( n = 80 \). This results in \( \frac{4.1}{\sqrt{80}} \approx 0.458 \) days. To satisfy the \( 10\% \) condition, we check if the sample size (80) is less than 10% of the population size. As long as the total number of students at the high school exceeds 800, the condition is met.